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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
23,771 datasets
A geospatial indicator describing the potential area-related flood risk for buildings in German territorial units. The data is provided by the Bundesamt für Kartographie und Geodäsie via WFS services. Registration is required to access the WCS and WFS services.
A collection of data and simulation results from a review of 37 articles reporting 278 Z-curve analysis applications. The dataset was created by Jolynn Pek and last updated in April 2026. It includes files for analysis, such as Excel spreadsheets and R scripts, totaling 10.9 MB.
A cross-sectional study of 133 adults from a systemically healthy Mexican population, classified into four periodontal health groups. The dataset contains salivary microbial profiles for 40 bacterial species, analyzed using Checkerboard DNA-DNA hybridization. It was authored by Paola E. García-Vázquez and last updated on 2026-04-30.
A transcriptomic dataset from figshare, last updated in 2026, identifies three small GTPase-associated biomarkers (ARHGEF3, S100A8, RHOU) for ulcerative colitis. The data was generated by Meilin Chen by integrating multiple transcriptomic datasets and validating findings with scRNA-seq and in vivo experiments. The dataset is small at 10.6 KB and is provided under a CC-BY-4.0 license.
A transcriptomic dataset from a study identifying small GTPase-associated biomarkers for ulcerative colitis. The data includes results from integrated transcriptomic datasets and single-cell RNA sequencing analyses, leading to the identification of three biomarkers (ARHGEF3, S100A8, RHOU). The dataset was authored by Meilin Chen and last updated on 2026-04-22.
Three biomarkers (ARHGEF3, S100A8, and RHOU) were identified for ulcerative colitis diagnosis using integrated transcriptomic data and machine learning. The supporting data, uploaded by Meilin Chen on April 22, 2026, is a 20.5 KB Excel file containing results from a study that validated these biomarkers across multiple independent cohorts. A diagnostic nomogram built from these markers achieved an AUC of 0.991 in the training set.
Three novel diagnostic biomarkers (ARHGEF3, S100A8, and RHOU) for ulcerative colitis were identified and validated across multiple independent cohorts. The supporting data, authored by Meilin Chen and last updated in April 2026, includes results from integrated transcriptomic datasets and single-cell RNA sequencing analyses. A diagnostic nomogram built using these biomarkers achieved an AUC of 0.991 in the training set.
An integrative multi-omics analysis of 85 KEGG metabolic pathways across TNBC and non-TNBC samples from TCGA, GEO, and 10X Genomics. Machine learning identified ST3GAL4 as a core enzyme linked to poor prognosis, with validation performed on 100 clinical samples. The dataset was authored by Yu Zhang and last updated on 2026-04-22.
A multi-omics dataset integrates bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data from TCGA, GEO, and 10X Genomics sources to investigate metabolic-immune mechanisms in triple-negative breast cancer (TNBC). The analysis, authored by Yu Zhang and last updated in April 2026, identifies ST3GAL4 as a core enzyme in lacto/neolacto glycosphingolipid metabolism linked to immune evasion and poor prognosis, validated with immunohistochemistry on 100 clinical samples.
A research document detailing the post-transcriptional regulation of G protein-coupled receptor kinase 2 (GRK2) by microRNA-181a in cardiomyocytes. The study, authored by Heidi Cho and shared under a CC-BY-4.0 license, includes methods such as miRNA microarray profiling, luciferase assays, and cellular stress experiments in mouse and rat models. The file was last updated on April 22, 2026.
Zhenyang Wang published this dataset on figshare in April 2026. It contains transcriptomic analysis data related to disulfidptosis, a novel cell death mechanism, in Ewing's sarcoma. The data was used to develop a five-gene prognostic risk signature and includes results from experimental validation in RD-ES cells.
A 5-gene risk model (NDUFS1, LRPPRC, NDUFA11, OXSM, NUBPL) derived from transcriptomic analysis of four GEO datasets stratifies Ewing's sarcoma patients by survival outcome. The dataset includes drug sensitivity predictions from GDSC and immune infiltration scores, validated experimentally in RD-ES cells. Author Zhenyang Wang published the data on figshare in April 2026.
Zhenyang Wang's research dataset, last updated April 15, 2026, contains transcriptomic analysis results for Ewing's sarcoma. The data includes differential expression of nine disulfidptosis-related genes, a five-gene prognostic risk model, and associated drug sensitivity and single-cell RNA sequencing analyses. The 3.3 MB dataset is available under a CC-BY-4.0 license.
A preliminary study of gut microbiota composition in 18 pediatric patients with Infantile Epileptic Spasms Syndrome (IESS), compared to focal epilepsy and healthy control groups. The dataset includes 16S ribosomal DNA sequencing results from fecal samples collected before and after adrenocorticotropic hormone (ACTH) treatment. The study was authored by Xuan Zhang and shared under a CC-BY-4.0 license on figshare in April 2026.
Mendelian randomization and kidney transcriptomic data from the GSE30529 study, integrated with experimental validation in db/db mice and primary cells. The dataset, authored by Mingliang Liu and last updated on 2026-04-15, supports the pathogenic role of the transcription factor ChREBP (MLXIPL) in diabetic kidney disease. It is a 15.9 KB DOCX file licensed under CC-BY-4.0.
A 15.7 KB DOCX file containing research data and analysis on the role of the ChREBP transcription factor in diabetic kidney disease. The dataset was authored by Mingliang Liu and last updated on April 15, 2026. It integrates genetic inference, kidney transcriptomic stratification, network analyses, and experimental validation from studies including GSE30529 and db/db mice.
A research document integrating Mendelian randomization, kidney transcriptomics, and experimental validation to investigate the role of the ChREBP transcription factor in diabetic kidney disease. The 16.4 KB file was authored by Mingliang Liu and last updated on April 15, 2026. It is shared under a CC-BY-4.0 license on the figshare platform.
Mingliang Liu published a 27.9 KB document on figshare in April 2026. The dataset describes an integrated study using Mendelian randomization, kidney transcriptomics, and experimental validation to investigate the role of the transcription factor ChREBP (MLXIPL) in diabetic kidney disease. It includes results from genetic inference, pathway analyses, and validation in mouse models and primary cells.
Mingliang Liu's 2026 study integrates Mendelian randomization, kidney transcriptomics, and experimental validation to identify the transcription factor ChREBP (MLXIPL) as a driver of metabolic remodeling in diabetic kidney disease. The 16.3 KB document, shared under a CC-BY-4.0 license, details methods and results from genetic inference, network analyses, and validation in db/db mice and primary cells. This work provides a mechanistic rationale for targeting the MLXIPL/ChREBP axis to mitigate residual renal risk.
Mingliang Liu published a research document on figshare in April 2026. The 18.1 KB DOCX file integrates two-sample Mendelian randomization, kidney transcriptomic stratification, network analyses, and experimental validation to identify ChREBP (MLXIPL) as a driver of maladaptive metabolic remodeling in diabetic kidney disease.